Digital resource scheduling and management methods and systems applied to urban transportation

Abstract

This invention provides a digital resource scheduling and management method and system for urban transportation, relating to the field of urban traffic management technology. First, it constructs a traffic carbon emission flow set integrating carbon emissions from fixed monitoring points, mobile transportation vehicles, and traffic operation status information. Based on the spatiotemporal distribution characteristics of the traffic carbon emission flow set, it generates a digital resource scheduling field containing digital resource distribution and scheduling potential energy information. It then establishes a field coupling relationship between the traffic carbon emission flow set and the digital resource scheduling field, generating a digital resource scheduling instruction set. This set of instructions is transmitted to a digital resource scheduling execution terminal, driving digital resource scheduling actions and generating a digital resource scheduling execution trajectory information set. Based on the digital resource scheduling execution trajectory information set, it adjusts the field parameters of the digital resource scheduling field, generating an optimized digital resource scheduling field scheme. This invention comprehensively considers traffic carbon emissions, achieving efficient digital resource scheduling.

Description

Technical Field

[0001] This invention relates to the field of urban traffic management technology, and more specifically, to a digital resource scheduling and management method and system applied to the field of urban traffic. Background Technology

[0002] In the urban transportation sector, with the acceleration of urbanization and the continuous growth of traffic flow, the problem of transportation carbon emissions is becoming increasingly serious, severely impacting the urban environment and the quality of life of residents. Traditional traffic management models mainly focus on traffic flow control and the construction of transportation facilities, paying relatively little attention to transportation carbon emissions and lacking effective carbon emission monitoring and management methods.

[0003] Currently, although some cities have set up some fixed carbon emission monitoring points, these points are limited in distribution and cannot comprehensively and dynamically reflect the overall carbon emission situation of urban transportation. At the same time, carbon emission monitoring of mobile transportation carriers (such as vehicles) mostly relies on the vehicle's own emission detection equipment, which has certain problems with the timeliness and accuracy of data acquisition, and lacks organic integration with traffic operation status.

[0004] In terms of digital resource scheduling and management, existing methods often fail to fully consider the key factor of traffic carbon emissions. The allocation and scheduling of digital resources are mainly based on conventional indicators such as traffic flow, resulting in low efficiency in the use of digital resources and an inability to effectively support the precise control of traffic carbon emissions. Summary of the Invention

[0005] In view of the aforementioned problems, and in conjunction with the first aspect of the present invention, embodiments of the present invention provide a digital resource scheduling and management method applied in the field of urban transportation, the method comprising: Construct a traffic carbon emission flow set in the urban transportation sector, which integrates carbon emission information from fixed monitoring points, carbon emission information from mobile transportation vehicles, and traffic operation status information. Based on the spatiotemporal distribution characteristics of the traffic carbon emission flow set, a digital resource scheduling field is generated, which includes the distribution status information of urban traffic digital resources and the scheduling potential information of urban traffic digital resources. Establish the field coupling relationship between the traffic carbon emission flow set and the digital resource scheduling field to generate a digital resource scheduling instruction set; The set of digital resource scheduling instructions is transmitted to the digital resource scheduling execution terminal to drive the digital resource scheduling execution actions and generate a set of digital resource scheduling execution trajectory information. Based on the set of digital resource scheduling execution trajectory information, the field parameters of the digital resource scheduling field are adjusted to generate an optimization scheme for the digital resource scheduling field.

[0006] Furthermore, embodiments of the present invention also provide a digital resource scheduling and management system applied in the field of urban transportation, comprising: A processor; a machine-readable storage medium for storing machine-executable instructions of the processor; wherein the processor is configured to execute the aforementioned digital resource scheduling and management method applied in the field of urban transportation by executing the machine-executable instructions.

[0007] In another aspect, embodiments of the present invention also provide a computer program product, the computer program product including machine-executable instructions, the machine-executable instructions being stored in a computer-readable storage medium, the processor of a computer device reading the machine-executable instructions from the computer-readable storage medium, the processor executing the machine-executable instructions, causing the computer device to execute the aforementioned digital resource scheduling and management method applied in the field of urban transportation.

[0008] Based on the above, a traffic carbon emission flow set integrating carbon emission information from fixed monitoring points, mobile transportation carriers, and traffic operation status information is constructed. A digital resource scheduling field is generated based on the spatiotemporal distribution characteristics of this traffic carbon emission flow set, presenting the distribution pattern and scheduling potential of urban traffic digital resources. Then, a field coupling relationship is established between the traffic carbon emission flow set and the digital resource scheduling field, generating a digital resource scheduling instruction set. This achieves organic linkage between traffic carbon emissions and digital resource scheduling, enabling precise allocation of digital resources based on actual carbon emissions and improving the efficiency of digital resource utilization. The digital resource scheduling instruction set is transmitted to the execution terminal, generating an execution trajectory information set. Based on the execution trajectory information set, the field parameters of the scheduling field are adjusted, and an optimization scheme is generated. This allows the entire digital resource scheduling system to continuously self-optimize and improve, adapting to the dynamic changes in urban traffic, ultimately achieving efficient and rational scheduling of urban traffic digital resources and effectively reducing traffic carbon emissions. Attached Figure Description

[0009] Figure 1 This is a schematic diagram of the execution flow of the digital resource scheduling and management method applied to the field of urban transportation provided in this embodiment of the invention.

[0010] Figure 2 This is a schematic diagram of exemplary hardware and software components of a digital resource scheduling and management system applied to the field of urban transportation, provided in an embodiment of the present invention. Detailed Implementation

[0011] The present invention will now be described in detail with reference to the accompanying drawings. Figure 1 This is a flowchart illustrating a digital resource scheduling and management method applied to the urban transportation field, provided by an embodiment of the present invention. The following is a detailed description of this digital resource scheduling and management method applied to the urban transportation field.

[0012] Step S110: Construct a traffic carbon emission flow set in the urban transportation sector, wherein the traffic carbon emission flow set integrates carbon emission information from fixed monitoring points, carbon emission information from mobile transportation vehicles, and traffic operation status information.

[0013] In this embodiment, to achieve effective scheduling and management of digital resources in the urban transportation sector, it is first necessary to construct a comprehensive and accurate set of transportation carbon emission flows. The construction of this set requires comprehensive consideration of information from multiple dimensions, including fixed monitoring points, mobile transportation vehicles, and traffic operation status, to ensure that the constructed set can fully reflect the carbon emission situation and related traffic operation characteristics in the urban transportation sector.

[0014] Step S111: Connect to the fixed carbon emission monitoring network, collect carbon emission monitoring information from different fixed monitoring points in the urban transportation sector over a continuous period of time, extract the temporal variation trend of carbon emission information at each fixed monitoring point and the spatial coverage of carbon emission information at each fixed monitoring point, and form a subset of carbon emission characteristics of fixed monitoring points.

[0015] In constructing the traffic carbon emission stream aggregator, the first step is to collect and process carbon emission information from fixed monitoring points. This involves connecting to the city's existing fixed carbon emission monitoring network, which covers multiple key locations within the urban transportation sector, such as major road intersections, transportation hubs, and areas surrounding commercial districts. Carbon emission monitoring data from these monitoring points is collected over continuous time periods, such as the past month, hourly. For each fixed monitoring point, the collected data over continuous time periods is analyzed to extract its temporal variation trend, i.e., the trend of carbon emission values ​​increasing, decreasing, or stabilizing over time. Simultaneously, the spatial coverage area of ​​each fixed monitoring point is determined. This spatial coverage area is determined based on factors such as the performance of the monitoring equipment, its installation location, and the surrounding environment. For example, the spatial coverage area of ​​a monitoring point installed beside a road might be a circular area with a certain radius centered on the monitoring point. The temporal variation trend and spatial coverage information of each fixed monitoring point are then compiled to form a fixed monitoring point carbon emission feature subset. This subset contains multiple entries, each corresponding to the relevant feature information of a fixed monitoring point.

[0016] Step S112: Connect to the mobile transportation carrier monitoring system, collect real-time carbon emission information and driving trajectory information of different types of mobile transportation carriers in the urban transportation field during the driving process, extract the trajectory correlation features of carbon emission information of each type of mobile transportation carrier and the emission intensity change trend of carbon emission information of each type of mobile transportation carrier, and form a subset of carbon emission features of mobile transportation carriers.

[0017] Next, a mobile transportation monitoring system is connected. This system can monitor various mobile transportation vehicles in the urban transportation sector, including buses, taxis, private cars, and trucks. Real-time carbon emission information of these vehicles during operation is collected through carbon emission monitoring devices installed on the vehicles. Simultaneously, the system collects the vehicle's trajectory information, obtained through technologies such as GPS, including the vehicle's path and location coordinates. For each type of mobile transportation vehicle, the correlation between carbon emission information and trajectory is analyzed, extracting trajectory correlation features, such as the correspondence between carbon emission changes at different road sections and speeds and the trajectory. Furthermore, the emission intensity change trend is extracted, i.e., the trend of carbon emission intensity changes per unit distance or unit time as the vehicle travels. The extracted trajectory correlation features and emission intensity change trends are summarized to form a subset of mobile transportation vehicle carbon emission features. This subset is categorized according to the type of mobile transportation vehicle, with each type corresponding to a set of features.

[0018] Step S113: Connect to the traffic operation monitoring platform, collect traffic flow information of different road sections, traffic flow information of different areas, traffic efficiency information of different road sections, traffic efficiency information of different areas, traffic congestion status information of different road sections, and traffic congestion status information of different areas within the urban traffic field. Calculate the time correlation coefficients of traffic flow information, traffic efficiency information, and traffic congestion status information in the same area at different time periods, and generate a time-domain correlation set. Calculate the spatial correlation coefficients of the above information among traffic flow information, traffic efficiency information, and traffic congestion status information in the same time period, and generate a spatial correlation set. Integrate the time-domain correlation set and the spatial correlation set to form a subset of traffic operation status features.

[0019] Then, the system connects to a traffic operation monitoring platform, which provides rich traffic operation status information within the urban transportation sector. It collects traffic flow information for different road segments (the number of vehicles passing through a segment per unit time) and traffic flow information for different areas (the total number of vehicles in a region per unit time). Simultaneously, it collects traffic efficiency information for different road segments, such as the average speed of vehicles passing through that segment; and traffic efficiency information for different areas, such as the overall average speed of vehicles within the area. It also collects traffic congestion situation information for different road segments, such as congestion duration and congestion level; and traffic congestion situation information for different areas, including congestion duration and congestion range. For the same area, at different time periods, it performs a time-domain correlation analysis on traffic flow information, traffic efficiency information, and traffic congestion situation information, calculating their time correlation coefficients. These time correlation coefficients reflect the degree of correlation between these information over time. The above time correlation coefficients are then summarized to generate a time-domain correlation set. Within the same time period, spatial correlation analysis is performed on traffic flow information, traffic efficiency information, and traffic congestion status information for different road segments or areas to calculate spatial correlation coefficients and generate a spatial correlation set. Finally, the temporal correlation set and the spatial correlation set are integrated to form a subset of traffic operation status features, which comprehensively reflects the temporal and spatial correlation characteristics of traffic operation status.

[0020] Step S114: Divide the fixed monitoring point carbon emission feature subset into zones according to spatial coverage, determine the zone boundaries, and generate a set of fixed carbon emission features for each zone. Each zone corresponds to a complete set of fixed monitoring point carbon emission feature information.

[0021] After obtaining the subset of carbon emission characteristics from fixed monitoring points, they need to be partitioned. Based on the spatial coverage of each fixed monitoring point, these points are classified into partitions according to their spatial location. First, the boundaries of the partitions are determined, taking into account factors such as the city's existing administrative divisions, road network distribution, and actual traffic management needs. For example, the city can be divided into grids, with each grid representing a partition, or the city can be divided into several areas based on major roads. Then, each fixed monitoring point is assigned to the corresponding partition based on its spatial coverage, ensuring that each partition contains all fixed monitoring points whose spatial coverage belongs to that partition. For each partition, the carbon emission characteristic information of all fixed monitoring points within it is integrated, including the temporal variation trends and spatial coverage of each monitoring point, forming a complete set of fixed monitoring point carbon emission characteristic information. The information from all partitions is then aggregated to generate a partitioned fixed carbon emission characteristic set.

[0022] Step S115: The carbon emission feature subset of the mobile transportation vehicle is partitioned and matched according to the region to which the driving trajectory belongs. The matching is completed by comparing the trajectory coordinates with the boundary coordinates of the partition, and a partitioned mobile carbon emission feature set is generated. Each partition corresponds to a complete set of mobile transportation vehicle carbon emission feature information.

[0023] For the subset of carbon emission characteristics of mobile transportation vehicles, zoning matching is performed according to the region to which the mobile transportation vehicle's trajectory belongs. The trajectory information of the mobile transportation vehicle contains a series of location coordinates. These trajectory coordinates are compared with the previously determined zone boundary coordinates to determine which zone the trajectory belongs to. For example, when a location coordinate in the trajectory of a mobile transportation vehicle falls within the boundary range of a certain zone, the carbon emission characteristics of that mobile transportation vehicle at that location and during the relevant time period are assigned to that zone. In this way, the information in the subset of carbon emission characteristics of mobile transportation vehicles is distributed to each zone. Each zone contains the carbon emission characteristics of all mobile transportation vehicles whose trajectories pass through that zone, such as trajectory association characteristics and emission intensity change trends. After integrating this information, a zone-specific mobile carbon emission characteristic set is generated.

[0024] Step S116: Divide the traffic operation status feature subset into regions, using the same boundary standard as the fixed monitoring point partitioning, and generate a set of traffic operation status features for each region. Each region corresponds to a complete set of traffic operation status feature information.

[0025] The partitioning of the traffic operation status feature subset adopts the same boundary standard as the fixed monitoring point partitioning to ensure consistency of various types of information across partitions and facilitate subsequent correlation and integration. According to this boundary standard, traffic flow information, traffic efficiency information, and traffic congestion status information for different road segments and areas within the traffic operation status feature subset are assigned to the corresponding partitions. For example, if a road segment belongs to partition A, then the traffic flow, traffic efficiency, and congestion status information for that road segment are all assigned to partition A. The traffic operation status feature information within each partition is integrated to form a complete set of traffic operation status feature information. The information from all partitions is then aggregated to generate a partition traffic operation status feature set.

[0026] Step S117: Merge the fixed carbon emission feature set of the partition with the mobile carbon emission feature set of the corresponding region to generate a comprehensive carbon emission feature set of the partition.

[0027] After constructing the regional fixed carbon emission feature sets and regional mobile carbon emission feature sets, they need to be merged to obtain a more comprehensive regional integrated carbon emission feature set. For each region, the information in the region's fixed carbon emission feature set and the corresponding regional mobile carbon emission feature set are integrated. During the fusion process, the characteristics and weights of different types of carbon emission information need to be considered. For example, carbon emission information from fixed monitoring points may be more stable and have a fixed coverage area, while carbon emission information from mobile transportation vehicles is dynamic and mobile. Through methods such as weighted averaging and feature splicing, the feature information of the two sets is merged to eliminate information redundancy and conflicts, forming a regional integrated carbon emission feature set that can comprehensively reflect the fixed and mobile carbon emission situation within the region.

[0028] Step S118: Link and integrate the regional comprehensive carbon emission feature set with the corresponding regional regional traffic operation status feature set, establish the mapping relationship between the two, and generate a regional traffic carbon emission flow subset.

[0029] Next, the comprehensive carbon emission feature set of each region is correlated and integrated with the corresponding regional traffic operation status feature set to establish a mapping relationship between the two, thereby generating a subset of regional traffic carbon emission flows. By analyzing the intrinsic connections between the comprehensive carbon emission features and the regional traffic operation status features—for example, increased traffic flow may lead to increased carbon emissions, and traffic congestion may affect the intensity of carbon emissions—the correlation patterns between them are determined. These correlation patterns are recorded in the form of mapping relationships, so that each feature in the comprehensive carbon emission feature set corresponds to the corresponding feature in the regional traffic operation status feature set. Through this correlation and integration, the originally independent carbon emission information and traffic operation status information are organically combined to form a subset of regional traffic carbon emission flows that reflects the relationship between traffic carbon emissions and traffic operation status.

[0030] Step S1181: Extract the peak carbon emission intensity information, the duration of carbon emission, and the spatial diffusion range of carbon emission from the integrated carbon emission feature set of the region.

[0031] When integrating the regional comprehensive carbon emission feature set with the regional traffic operation status feature set, key information is first extracted from the regional comprehensive carbon emission feature set. This includes peak carbon emission intensity information, i.e., the maximum carbon emission intensity reached by the region within a certain period; carbon emission duration information, i.e., the duration for which carbon emission intensity remains at a high level or within a specific range; and carbon emission spatial diffusion range information, i.e., the size and shape of the diffusion range of carbon emissions from the source to the surrounding space. This information can describe the characteristics of regional comprehensive carbon emissions from different perspectives.

[0032] Step S1182: Extract peak traffic flow information, duration of traffic congestion, and spatial distribution range of traffic flow from the regional traffic operation status feature set.

[0033] Meanwhile, key information is extracted from the regional traffic operation status feature set, including peak traffic flow information, which is the maximum traffic flow reached in the region within a certain period; traffic congestion duration information, which is the duration of traffic congestion in the region; and traffic flow spatial distribution information, which is the spatial distribution of traffic flow in the region, such as which areas have high traffic flow and which areas have low traffic flow. The above information corresponds to the information extracted from the regional comprehensive carbon emission feature set, which facilitates the correlation analysis between the two.

[0034] Step S1183: Perform time alignment processing on the peak carbon emission intensity information and the peak traffic flow information to determine the corresponding relationship between the two in the time dimension.

[0035] The extracted peak carbon emission intensity and peak traffic flow information were time-aligned. Since both were collected over a specific period, there may be temporal discrepancies. Time alignment unifies their timelines to accurately analyze their temporal correspondence. For example, it determines whether the peak carbon emission intensity coincides with the peak traffic flow, or whether there is a time lag, thus establishing their temporal correlation.

[0036] Step S1184: Compare and analyze the carbon emission duration information with the traffic congestion duration information to determine the correlation characteristics between the two over time.

[0037] A comparative analysis is conducted on the duration of carbon emissions and the duration of traffic congestion to compare their time spans and trends. For example, the analysis examines whether the duration of carbon emissions increases when the duration of traffic congestion increases, or whether there are other specific correlation patterns between the two, thereby determining their correlation characteristics over time.

[0038] Step S1185: Spatially overlay the information on the spatial diffusion range of carbon emissions with the information on the spatial distribution range of traffic flow to determine the overlapping area of ​​the two in the spatial dimension and the coverage relationship between the two in the spatial dimension.

[0039] The spatial overlay process combines information on the spatial diffusion range of carbon emissions and the spatial distribution range of traffic flow, displaying their spatial extents on the same coordinate system. This method allows for the intuitive identification of overlapping areas in space—that is, areas that belong to both the carbon emission spatial diffusion range and the traffic flow spatial distribution range. Furthermore, it analyzes the coverage relationship between the two, such as whether the traffic flow spatial distribution range completely encompasses the carbon emission spatial diffusion range, or whether there is partial overlap between the two.

[0040] Step S1186: Using time correspondence, time span, and spatial coverage as input features, and using known carbon emission and traffic operation status association labels from historical data as training targets, construct and train a neural network model as the association model between regional comprehensive carbon emission features and regional traffic operation status features.

[0041] Using the time-correspondence, time-span, and spatial coverage association features obtained in steps S1183 to S1185 as input features, and collecting known carbon emission and traffic operation status association labels from historical data as training targets, a neural network model is constructed. This neural network model can employ a multilayer perceptron structure, including an input layer, hidden layers, and an output layer. The input layer receives the aforementioned input features, the hidden layer processes and transforms the features through multiple neurons, and the output layer outputs the association prediction results between carbon emissions and traffic operation status. During training, the model is trained using a large amount of historical data. By adjusting the model's parameters, the model's prediction results are made as close as possible to the known association labels, thereby obtaining an association model that accurately reflects the relationship between the comprehensive carbon emission characteristics and the traffic operation status characteristics of a given region.

[0042] Step S1187: Substitute the specific data from the corresponding region's integrated carbon emission feature set and the corresponding region's traffic operation status feature set into the correlation model to generate correlation data between carbon emissions and traffic operation status within the region.

[0043] After the correlation model is trained, specific data from the corresponding region's comprehensive carbon emission feature set and regional traffic operation status feature set are substituted into the model. The model processes and calculates the input data to generate correlation data between carbon emissions and traffic operation status within the region. This data can quantitatively represent the degree of correlation and the mode of influence between the two, such as the extent to which a change in a certain traffic operation status parameter will lead to a change in carbon emission parameters.

[0044] Step S1188: Extract the core correlation information from the correlation data. The core correlation information includes the way carbon emission changes affect traffic operation status and the way traffic operation status responds to carbon emissions.

[0045] Extracting core correlation information from the generated data is crucial for understanding the interaction between carbon emissions and traffic conditions. This includes how changes in carbon emissions affect traffic conditions, such as whether increased carbon emissions lead to decreased traffic efficiency; and how traffic conditions feedback on carbon emissions, such as whether traffic congestion further exacerbates carbon emissions. By extracting this core correlation information, we can gain a deeper understanding of the intrinsic relationship between the two.

[0046] Step S1189: Integrate the core association information with the corresponding regional comprehensive carbon emission feature set and the corresponding regional traffic operation status feature set to form a basic data framework for regional traffic carbon emission flow. The basic data framework includes data field definitions and data association rules.

[0047] The extracted core correlation information is integrated with the corresponding regional integrated carbon emission characteristic set and regional traffic operation status characteristic set. During the integration process, the definitions of each data field are clarified, such as the name, meaning, and data type of each feature parameter. Simultaneously, data association rules are formulated, specifying the association methods and constraints between different data fields. Through this integration, a basic data framework for regional traffic carbon emission flows is formed.

[0048] Step S11810: Supplement the basic data framework with regional identifiers, time identifiers, and data source identifiers to generate a subset of traffic carbon emission streams by region.

[0049] In the basic data framework of regional traffic carbon emission flows, regional identifiers are added to clarify the region to which the data framework belongs; time identifiers are added to record the time range corresponding to the data; and data source identifiers are added to indicate whether the data comes from fixed monitoring points, mobile transportation vehicles, or traffic operation monitoring platforms, etc. By adding these identifiers, the data framework becomes more complete and clear, ultimately generating a subset of regional traffic carbon emission flows.

[0050] Step S119: Add a regional code identifier and a timestamp identifier to each traffic carbon emission stream subset of each zone to determine the spatial range and time span corresponding to each traffic carbon emission stream subset of each zone.

[0051] After generating subsets of traffic carbon emission flows by region, a regional code identifier and a timestamp identifier are added to each subset. The regional code identifier is a unique code assigned to each region based on the previously determined region boundaries and region rules, used to distinguish different regions. The timestamp identifier records the time range corresponding to the traffic carbon emission flow subset of that region; for example, a subset corresponds to the carbon emission flow information of a certain day. Simultaneously, the spatial range corresponding to each subset of traffic carbon emission flows by region is clearly defined, i.e., the boundary range of that region; and the corresponding time span, i.e., the length of time the data covers. By adding these identifiers and defining the range and span, the information of each subset of traffic carbon emission flows by region becomes more explicit and easier to manage.

[0052] Step S1110: Integrate all subsets of regional traffic carbon emission flows with regional code identifiers and timestamp identifiers to form a set of traffic carbon emission flows.

[0053] Finally, all subsets of traffic carbon emission flows from different zones, each with a regional code and timestamp, are integrated. Following the order of regional code and timestamp, the data from each subset is summarized and organized, eliminating duplication and conflicts to form a complete set of traffic carbon emission flows covering the entire urban transportation sector. This set of traffic carbon emission flows contains traffic carbon emission flow information for each zone at different times.

[0054] Step S120: Based on the spatiotemporal distribution characteristics of the traffic carbon emission flow set, a digital resource scheduling field is generated. The digital resource scheduling field includes the distribution status information of urban traffic digital resources and the scheduling potential information of urban traffic digital resources.

[0055] After constructing the traffic carbon emission flow set, a digital resource scheduling field is generated based on the spatiotemporal distribution characteristics of this set. The digital resource scheduling field is crucial for the effective scheduling of digital resources; it reflects the distribution of urban traffic digital resources and their scheduling potential and trends. By analyzing the temporal and spatial distribution characteristics of the traffic carbon emission flow set, the distribution pattern and scheduling potential of digital resources are determined.

[0056] Step S121: Extract the temporal distribution characteristics of the traffic carbon emission flow set, determine the activity level of the traffic carbon emission flow in different time periods, and form a set of carbon emission flow temporal activity characteristics.

[0057] First, a time-dimensional analysis is performed on the traffic carbon emission flow dataset to extract its temporal distribution characteristics. The data in the traffic carbon emission flow dataset is divided according to time, such as into different time periods, like hourly, daily, and weekly periods. For each time period, the activity level of the traffic carbon emission flow is analyzed. Activity level can be measured by indicators such as the total amount of carbon emissions, the frequency of change, and the number of peak occurrences within that time period. The activity level information from different time periods is then compiled to form a carbon emission flow temporal activity characteristic set. This set of carbon emission flow temporal activity characteristics reflects the temporal variation patterns and activity levels of traffic carbon emissions.

[0058] Step S122: Extract the spatial distribution characteristics of the traffic carbon emission flow set, determine the degree of aggregation of traffic carbon emission flows in different regions, and form a set of spatial aggregation characteristics of carbon emission flows.

[0059] Next, a spatial analysis of the traffic carbon emission flow set is conducted to extract its spatial distribution characteristics. Based on information from each zone within the traffic carbon emission flow set, the distribution of traffic carbon emission flows in different regions is analyzed to determine their degree of aggregation. The degree of aggregation can be measured by indicators such as the number of carbon emission flows per unit area, distribution density, and whether aggregation centers are formed. For example, some regions may have a higher degree of carbon emission flow aggregation due to high traffic volume and dense mobile transportation vehicles, while other regions may have a lower degree of aggregation. The aggregation information from different regions is then compiled to form a set of spatial aggregation characteristics of carbon emission flows.

[0060] Step S123: Integrate the set of temporal active features of carbon emission flows with the set of spatial clustering features of carbon emission flows to generate a set of spatiotemporal distribution features of carbon emission flows.

[0061] The process involves fusing the temporal activity characteristics and spatial aggregation characteristics of carbon emission flows. This fusion process organically combines temporal and spatial features, such as analyzing changes in carbon emission flow aggregation levels across different time periods or the temporal variation of carbon emission flow activity levels within different regions. Through this fusion, a spatiotemporal distribution characteristic set of carbon emission flows is generated that comprehensively reflects the temporal and spatial distribution characteristics of transportation carbon emission flows.

[0062] Step S124: Connect to the digital resource management platform and collect information on the type of all schedulable digital resources in the urban transportation field, the storage location information of all schedulable digital resources, and the availability status information of all schedulable digital resources. The types of schedulable digital resources cover traffic signal control resources, traffic information push resources, and charging pile scheduling resources, forming a set of basic digital resource information.

[0063] To generate a digital resource scheduling field, it is necessary to acquire relevant information on schedulable digital resources within the urban transportation sector. This involves connecting to a digital resource management platform, which stores detailed information on all schedulable digital resources. The platform collects information on the type of these resources, such as traffic signal control resources, traffic information push resources, and charging pile scheduling resources; it also stores location information, i.e., the physical or network location of these digital resources; and availability status information, including whether the resource is available, its current load, and its estimated availability duration. This collected information is then organized to form a basic set of digital resource information.

[0064] Step S125: Based on the digital resource basic information set, extract the service range characteristics and scheduling response characteristics of each digital resource to form a digital resource scheduling characteristic set. The service range characteristics are defined by geographical range coordinates, and the scheduling response characteristics are characterized by response time parameters.

[0065] From the set of basic information on digital resources, service range characteristics and dispatch response characteristics are extracted for each type of digital resource. Service range characteristics refer to the geographical area that the digital resource can cover, defined by geographical coordinates. For example, the service range of a traffic signal control resource might be the coordinate range of an intersection and its surrounding area. Dispatch response characteristics refer to the time it takes for a digital resource to respond after receiving a dispatch instruction, characterized by a response time parameter. For example, the response time of a traffic information push resource might be the time required from receiving the push instruction to successfully pushing the information. The extracted service range characteristics and dispatch response characteristics are then organized according to the digital resource type to form a digital resource dispatch feature set.

[0066] Step S126: Perform feature matching between the spatiotemporal distribution feature set of carbon emission flows and the feature set of digital resource scheduling, calculate the similarity between the spatiotemporal feature vector of carbon emission flows and the feature vector of digital resource scheduling, mark feature pairs with similarity higher than a preset threshold as adaptation relationships, and generate an adaptation relationship list.

[0067] Feature matching is performed between the spatiotemporal distribution feature set of carbon emission flows and the feature set of digital resource scheduling. First, the features in both sets are vectorized to form spatiotemporal feature vectors of carbon emission flows and feature vectors of digital resource scheduling. Then, the similarity between these two vectors is calculated using methods such as cosine similarity or Euclidean distance. A similarity threshold is preset; when the similarity between two feature vectors exceeds this threshold, they are considered to have a fit relationship, and this pair of features is marked as a fit relationship pair. All fit relationship pairs are summarized to generate a fit relationship list, which records which spatiotemporal distribution features of carbon emission flows fit with which digital resource scheduling features.

[0068] Step S127: Based on the adaptation relationship list, divide the digital resource scheduling areas, determine the area boundaries according to the differences in the type of adaptation relationship and the geographical continuity of the area, and each digital resource scheduling area corresponds to a set of adapted carbon emission flow spatiotemporal distribution characteristics and digital resource types.

[0069] Digital resource scheduling zones are divided based on a list of adaptation relationships. First, the differences in the types of adaptation relationships are analyzed, as different types of adaptation relationships may correspond to different scheduling needs and resource allocations. Simultaneously, regional geographical continuity is considered to ensure that the divided scheduling zones are geographically continuous, facilitating resource management and scheduling. Based on the type of adaptation relationships and geographical continuity, regional boundaries are determined, dividing the urban transportation sector into multiple digital resource scheduling zones. Each digital resource scheduling zone corresponds to a set of adapted spatiotemporal distribution characteristics of carbon emission flows and digital resource types; that is, digital resource scheduling within this zone needs to consider the corresponding spatiotemporal distribution characteristics of carbon emission flows and configure adapted digital resource types.

[0070] Step S1271: Based on the set of spatiotemporal distribution characteristics of carbon emission flows, perform preliminary partitioning of the urban transportation sector to ensure consistency in the spatiotemporal distribution characteristics of carbon emission flows within each preliminary partition.

[0071] Before dividing the digital resource scheduling areas based on the adaptation relationship list, the urban transportation sector is first preliminarily partitioned based on the spatiotemporal distribution characteristics set of carbon emission flows. The characteristics of each region in the spatiotemporal distribution characteristics set of carbon emission flows are analyzed, and regions with similar characteristics are grouped into the same preliminary partition, ensuring a high degree of consistency in the spatiotemporal distribution characteristics of carbon emission flows within each preliminary partition. For example, adjacent regions with similar characteristics such as the activity level and aggregation degree of carbon emission flows are grouped into one preliminary partition.

[0072] Step S1272: Extract the first core information of the spatiotemporal distribution characteristics of carbon emission flows in each preliminary partition to form the feature identifier of each preliminary partition. The first core information includes the feature peak value, feature duration, and feature spatial coverage.

[0073] For each preliminary zone, the first core information of its spatiotemporal distribution characteristics of carbon emission flows is extracted. This first core information includes: characteristic peak value (the maximum value of carbon emission flows in time or space within the zone); characteristic duration (the duration of the characteristic peak value or a specific characteristic level); and characteristic spatial coverage (the spatial range covered by carbon emission flows within the zone). These first core information are combined to form the feature identifier for each preliminary zone, uniquely identifying the spatiotemporal distribution characteristics of carbon emission flows within that zone.

[0074] Step S1273: Based on the digital resource scheduling feature set, classify and group the digital resources, and extract the second core information of the scheduling features of each group of digital resources to form a resource identifier for each group of digital resources. The second core information includes the service range radius, scheduling response time, and resource capacity.

[0075] Based on the set of digital resource scheduling characteristics, digital resources are classified and grouped. For example, digital resources with similar characteristics such as service range and scheduling response time are grouped together. For each group of digital resources, the second core information of its scheduling characteristics is extracted, including service range radius (the radius of the resource's service area); scheduling response time (the time it takes for the resource to respond to scheduling instructions); and resource capacity (the maximum number of tasks or users the resource can handle). This second core information is combined to form the resource identifier for each group of digital resources.

[0076] Step S1274: Match the feature identifier of each preliminary partition with the resource identifier of each group of digital resources, and match the corresponding digital resource group for each preliminary partition according to the determined adaptation relationship.

[0077] The feature identifier of each preliminary partition is matched with the resource identifier of each group of digital resources. Referring to the previously generated adaptation relationship list, it is determined which digital resource groups are compatible with the feature identifier of the preliminary partition. Based on the adaptation relationship, a corresponding digital resource group is matched for each preliminary partition, so that the spatiotemporal distribution characteristics of carbon emission flows within the partition can be adapted to the scheduling characteristics of the matched digital resource groups.

[0078] Step S1275: Count the number of matching digital resource groups in each preliminary partition, calculate the standard deviation of the storage location coordinates of each digital resource group in each preliminary partition, and define it as the resource distribution uniformity parameter; compare the resource distribution uniformity parameter of each preliminary partition with a preset dispersion threshold; when the resource distribution uniformity parameter of any preliminary partition is greater than the dispersion threshold, based on the storage location coordinates of the digital resource groups in the preliminary partition, use a spatial clustering algorithm to spatially divide the preliminary partition and generate multiple subdivided regions.

[0079] The number of matching digital resource groups within each initial partition is counted, and the standard deviation of the storage location coordinates of each digital resource group is calculated. This standard deviation is defined as the resource distribution uniformity parameter. The resource distribution uniformity parameter reflects the evenness of the distribution of digital resource groups within the initial partition; a larger standard deviation indicates a more dispersed distribution. This parameter is compared with a preset dispersion threshold. When the resource distribution uniformity parameter is greater than the dispersion threshold, it indicates that the distribution of digital resource groups within the initial partition is too dispersed, which is not conducive to resource scheduling and management. In this case, based on the storage location coordinates of the digital resource groups, a spatial clustering algorithm, such as K-means clustering, is used to spatially divide the initial partition into multiple sub-regions, making the distribution of digital resource groups relatively concentrated within each sub-region.

[0080] Step S1276: When the digital resource group identifier lists of two adjacent preliminary partitions are completely identical, calculate the Euclidean distance between the spatiotemporal distribution feature vectors of carbon emission flows of the two preliminary partitions and define it as a feature difference parameter; compare the feature difference parameter with a preset merging threshold; when the feature difference parameter is less than the merging threshold, merge the two adjacent preliminary partitions into one partition.

[0081] For two adjacent preliminary partitions, if their lists of matching digital resource group identifiers are completely identical, it indicates that these two partitions have the same type of demand for digital resources. In this case, the Euclidean distance between the spatiotemporal distribution feature vectors of carbon emission flows in these two preliminary partitions is calculated, and this distance is defined as the feature difference parameter. The feature difference parameter reflects the similarity of the spatiotemporal distribution features of carbon emission flows in the two partitions; the smaller the distance, the more similar the features. This parameter is compared with a preset merging threshold. When the feature difference parameter is less than the merging threshold, it indicates that the spatiotemporal distribution features of carbon emission flows in these two adjacent preliminary partitions are also relatively similar, and they can be merged into one partition to reduce the number of partitions and improve management efficiency.

[0082] Step S1277: For each adjusted partition, re-extract the core information of its spatiotemporal distribution characteristics of carbon emission flow and the digital resource group information that matches each adjusted partition. Substitute the re-extracted information into the preset adaptation verification model to generate adaptation verification results. Assign a unique regional code to each partition whose adaptation verification results meet the requirements. The regional code includes the spatial location information of the partition and the main adaptation resource type information of the partition.

[0083] After subdividing or merging the initial partitions, the core information of the spatiotemporal distribution characteristics of carbon emission flows and the matching digital resource group information are re-extracted for each adjusted partition. This re-extracted information is then substituted into a pre-defined adaptation verification model, which verifies the degree of fit between the spatiotemporal distribution characteristics of the partition's carbon emission flows and the matching digital resource group. Based on the adaptation verification results generated by the model, a unique regional code is assigned to partitions that meet the requirements. The regional code contains the partition's spatial location information, such as the city region and coordinate range, as well as the partition's main compatible resource type information, to facilitate rapid identification and management of the partitions.

[0084] Step S1278: Record the regional code of each partition, the core information of the spatiotemporal distribution characteristics of carbon emission flows of each partition, the digital resource group information matched by each partition, and the regional boundary information of each partition. The regional boundary information is represented by a geographic coordinate sequence.

[0085] The system records the regional code, spatiotemporal distribution characteristics of carbon emission flows, matching digital resource group information, and regional boundary information for each partition. Regional boundary information is represented by a geographic coordinate sequence, that is, the coordinates of each point on the partition boundary are recorded sequentially to accurately describe the spatial extent of the partition. This recorded information will serve as a crucial basis for the subsequent generation of the digital resource scheduling field.

[0086] Step S128: Normalize the distribution density of digital resources in each digital resource scheduling area to generate a digital resource distribution density index; normalize the carbon emission flow activity level in each digital resource scheduling area to generate a carbon emission flow activity index; based on the digital resource distribution density index and the carbon emission flow activity index, determine the initial value of the scheduling potential energy of digital resources in each digital resource scheduling area through a correlation function.

[0087] After delineating the digital resource scheduling regions, it is necessary to determine the initial value of the scheduling potential energy for digital resources within each region. First, the distribution density of digital resources within each region is normalized to obtain a digital resource distribution density index. Simultaneously, the carbon emission flow activity level of each region is normalized to obtain a carbon emission flow activity index. Then, these two indices are combined using a correlation function to calculate the initial value of the scheduling potential energy for digital resources within each region. This initial scheduling potential energy reflects the potential capacity and trend of digital resource scheduling within that region.

[0088] Step S1281: Count the number of various types of digital resources in each digital resource scheduling area and determine the total number of digital resources in each digital resource scheduling area.

[0089] To calculate the distribution density of digital resources, we first count the quantity of each type of digital resource within each digital resource scheduling area, such as the quantity of traffic signal control resources, traffic information push resources, and charging pile scheduling resources. Then, we sum the quantities of each type of digital resource to obtain the total quantity of digital resources in each digital resource scheduling area.

[0090] Step S1282: Measure the spatial area of ​​each digital resource scheduling area, calculate the number of digital resources per unit area in each digital resource scheduling area, and use the number of digital resources per unit area as the initial value of the distribution density of digital resources in the digital resource scheduling area.

[0091] The spatial area of ​​each digital resource scheduling area is measured, and the spatial area can be calculated using the geographic coordinates of the area boundary. Then, the total number of digital resources is divided by the spatial area to obtain the number of digital resources per unit area, and this number is used as the initial value of the digital resource distribution density for that digital resource scheduling area.

[0092] Step S1283: Obtain the location coordinates of all digital resource storage points within each digital resource scheduling area; centered on each resource storage point, set a neighborhood with a fixed radius, count the number of resource storage points in each neighborhood, calculate the average number of resource points in all neighborhoods, and define it as the local cluster density; compare the local cluster density with a preset distribution threshold; when the local cluster density is lower than the distribution threshold, multiply the initial distribution density value by a density correction coefficient greater than 1 to obtain the digital resource distribution density correction value, wherein the density correction coefficient is negatively correlated with the spatial standard deviation of the resource storage point coordinates.

[0093] Obtain the location coordinates of all digital resource storage points within each digital resource scheduling area. For each resource storage point, define a neighborhood with a fixed radius, such as a circular neighborhood with a radius of 500 meters. Count the number of resource storage points in each neighborhood, and then calculate the average number of resource points across all neighborhoods. Define this average as the local cluster density. Compare the local cluster density with a preset distribution threshold. If the local cluster density is lower than the distribution threshold, it indicates that the resource storage points are relatively dispersed, and the initial distribution density value needs to be corrected. In this case, multiply the initial distribution density value by a density correction coefficient greater than 1 to obtain the corrected digital resource distribution density value. The density correction coefficient is negatively correlated with the spatial standard deviation of the resource storage point coordinates; that is, the larger the spatial standard deviation, the more dispersed the resource distribution, and the larger the density correction coefficient, to appropriately compensate for the dispersed resource distribution.

[0094] Step S1284: Normalize the digital resource distribution density correction value to generate a dimensionless digital resource distribution density index.

[0095] The digital resource distribution density correction value is normalized to transform it into a dimensionless digital resource distribution density index. The normalization process can employ the min-max normalization method, mapping the distribution density correction value to the range of 0-1, thus ensuring the comparability of digital resource distribution density indices across different regions.

[0096] Step S1285: Extract the activity level parameter from the spatiotemporal distribution feature set of carbon emission flows in each digital resource scheduling area. The activity level parameter includes the flow rate of carbon emission flows and the aggregation intensity of carbon emission flows.

[0097] Activity parameters are extracted from the spatiotemporal distribution characteristics of carbon emission flows in each digital resource scheduling region. These parameters include the flow rate and aggregation intensity of the carbon emission flows. The flow rate refers to the spatial displacement of the carbon emission flow per unit time, and the aggregation intensity refers to the concentration of the carbon emission flow per unit space.

[0098] Step S1286: Normalize the activity level parameter to generate a dimensionless carbon emission flow activity index, and construct a correlation function between the digital resource distribution density index and the carbon emission flow activity index. The correlation function reflects the combined effect of the two on the scheduling potential energy, and the correlation function adopts a linear weighted function form.

[0099] The extracted activity parameters are normalized using the min-max normalization method to generate a dimensionless carbon emission flow activity index. Then, a correlation function is constructed between the digital resource distribution density index and the carbon emission flow activity index. This correlation function is a linear weighted function, where the initial scheduling potential energy equals the digital resource distribution density index multiplied by a weighting coefficient plus the carbon emission flow activity index multiplied by another weighting coefficient. The weighting coefficients are set based on actual conditions and experience to reflect the combined effect of both factors on the scheduling potential energy.

[0100] Step S1287: Substitute the digital resource distribution density index and the carbon emission flow activity index of each digital resource scheduling area into the correlation function to calculate the initial value of the digital resource scheduling potential energy of each digital resource scheduling area.

[0101] The digital resource distribution density index and carbon emission flow activity index of each digital resource scheduling region are substituted into the constructed correlation function for calculation to obtain the initial value of the digital resource scheduling potential energy of each region.

[0102] Step S1288: When the initial value of the scheduling potential energy exceeds the reasonable range threshold, adjust the parameter weights in the correlation function, resubmit them into the correlation function to calculate the initial value of the scheduling potential energy, until the calculation result falls within the reasonable range threshold.

[0103] Set a reasonable threshold range for the initial scheduling potential energy. If the calculated initial scheduling potential energy exceeds this threshold, the parameter weights in the correlation function need to be adjusted. By repeatedly adjusting the weight coefficients and resubmitting them into the correlation function for calculation, the initial scheduling potential energy is calculated until it falls within the reasonable threshold range.

[0104] Step S1289: Record the digital resource distribution density correction value, carbon emission flow activity index and initial scheduling potential energy value of each digital resource scheduling area to form the basic data of scheduling potential energy of each digital resource scheduling area.

[0105] Record the digital resource distribution density correction value, carbon emission flow activity index, and the calculated initial value of scheduling potential energy for each digital resource scheduling area to form the basic data of scheduling potential energy.

[0106] Step S129: Based on the digital resource distribution density of each digital resource scheduling area, the initial value of the scheduling potential energy of each digital resource scheduling area, and the spatiotemporal distribution characteristics of the adapted carbon emission flow, construct a digital resource scheduling subfield for each digital resource scheduling area. The digital resource scheduling subfield includes a resource distribution layer, a potential energy gradient layer, and an adaptation relationship layer.

[0107] Based on the digital resource distribution density, initial scheduling potential energy, and spatiotemporal distribution characteristics of the adapted carbon emission streams in each digital resource scheduling area, a digital resource scheduling subfield is constructed. The digital resource scheduling subfield comprises multiple layers: a resource distribution layer displays the distribution of digital resources within the area, using different colors or symbols to represent different types of digital resources and their locations; a potential energy gradient layer displays the spatial distribution gradient of the scheduling potential energy, reflecting the differences in scheduling potential energy at different locations; and an adaptation relationship layer displays the adaptation relationship between the spatiotemporal distribution characteristics of carbon emission streams and the characteristics of digital resource scheduling. These layers together constitute the digital resource scheduling subfield.

[0108] Step S1210: Integrate the digital resource scheduling subfields of all digital resource scheduling areas, supplement the boundary association information between each digital resource scheduling subfield and the resource flow channel information between each digital resource scheduling subfield, and generate a digital resource scheduling field. The resource flow channel information is characterized by channel bandwidth and transmission delay parameters.

[0109] All digital resource scheduling sub-fields within the digital resource scheduling area are integrated to form a complete digital resource scheduling field. During the integration process, boundary association information between each sub-field is supplemented, clarifying the connection relationships and interaction methods between adjacent sub-fields. Simultaneously, resource flow channel information is supplemented; these channels are the paths through which digital resources flow between different sub-fields. Channel performance is characterized by parameters such as channel bandwidth and transmission delay. For example, channel bandwidth represents the amount of resources that can be transmitted per unit time, and transmission delay represents the time required for resources to traverse the channel. By integrating the sub-fields and supplementing the information, the final digital resource scheduling field is generated.

[0110] Step S130: Establish the field coupling relationship between the traffic carbon emission flow set and the digital resource scheduling field, and generate a digital resource scheduling instruction set.

[0111] After generating the digital resource scheduling field, it is necessary to establish the field coupling relationship between the traffic carbon emission flow set and the digital resource scheduling field. By analyzing the interaction and influence between the two, the correlation pattern and degree between them are determined, thereby generating a digital resource scheduling instruction set. This digital resource scheduling instruction set contains specific instructions for scheduling digital resources to guide the actual resource scheduling execution process.

[0112] Step S131: Extract key feature parameters from the traffic carbon emission flow set. The key feature parameters include the flow rate of the carbon emission flow, the aggregation intensity of the carbon emission flow, and the spatiotemporal diffusion direction of the carbon emission flow. The flow rate is calculated by the spatial displacement of the carbon emission flow per unit time, and the aggregation intensity is characterized by the concentration value of the carbon emission flow per unit space.

[0113] Key characteristic parameters are extracted from the ensemble of traffic carbon emission flows. These parameters reflect the important characteristics of traffic carbon emission flows. Among them, the flow rate is calculated by the spatial displacement of the carbon emission flow per unit time, such as the distance the carbon emission flow moves in one hour; the aggregation intensity is characterized by the concentration value of the carbon emission flow per unit space, that is, the carbon emission flow content per unit volume or unit area; and the spatiotemporal diffusion direction refers to the diffusion trend and direction of the carbon emission flow in time and space.

[0114] Step S132: Extract key field parameters in the digital resource scheduling field. The key field parameters include the scheduling potential energy of digital resources, the distribution density of digital resources, and the circulation efficiency of digital resources. The circulation efficiency is calculated by the amount of resources circulating per unit time.

[0115] Key field parameters are extracted from the digital resource scheduling field, reflecting its characteristics. Scheduling potential energy is a previously calculated parameter reflecting the resource scheduling potential; distribution density is the distribution of digital resources in the scheduling field; and flow efficiency is calculated by the amount of resources flowed per unit time, i.e., the amount of resources transmitted through the resource flow channel per unit time.

[0116] Step S133: Construct a correlation mapping matrix between key feature parameters of traffic carbon emission flow and key field parameters of digital resource scheduling field. The row dimension of the correlation mapping matrix is ​​the key feature parameters of carbon emission flow, and the column dimension is the key field parameters, so as to determine the corresponding correlation between different feature parameters and different field parameters.

[0117] Construct a correlation mapping matrix where the row dimension represents the key feature parameters of traffic carbon emission flows, and the column dimension represents the key field parameters of the digital resource scheduling field. The elements in the matrix represent the correlation method and degree between the corresponding row and column parameters, for example, through correlation coefficients or weight values. This correlation mapping matrix can determine the corresponding correlation methods between the key feature parameters of different carbon emission flows and the key parameters of different fields.

[0118] Step S134: Based on the correlation mapping matrix, calculate the field coupling coefficient between the traffic carbon emission flow set and the digital resource scheduling field. The field coupling coefficient characterizes the degree of correlation between the traffic carbon emission flow set and the digital resource scheduling field.

[0119] Based on the correlation mapping matrix, the field coupling coefficient between the traffic carbon emission flow set and the digital resource scheduling field is calculated. The calculation of the field coupling coefficient comprehensively considers the correlation degree of each element in the correlation mapping matrix, obtaining a comprehensive coefficient value through weighted summation and other methods. This coefficient value characterizes the degree of correlation between the traffic carbon emission flow set and the digital resource scheduling field; the larger the coefficient value, the stronger the correlation between the two.

[0120] Step S135: Obtain a preset coupling coefficient threshold, compare the field coupling coefficient with the coupling coefficient threshold, and filter out traffic carbon emission flow segments and their mapped digital resource scheduling field sub-regions with field coupling coefficients higher than the coupling coefficient threshold.

[0121] A preset coupling coefficient threshold is established, which is set based on actual application needs and experience. The calculated field coupling coefficient is compared with this threshold, and traffic carbon emission flow segments with field coupling coefficients higher than the threshold, as well as the digital resource scheduling field sub-regions mapped by these segments, are selected. These highly coupled segments and sub-regions are the focus of resource scheduling because they are closely related, and the effect of resource scheduling may be more significant.

[0122] Step S136: For each selected traffic carbon emission flow segment and its corresponding digital resource scheduling sub-region, determine the scheduling direction and scale of the digital resources. The scheduling direction is determined based on the flow direction of the carbon emission flow, and the scheduling scale is determined based on the aggregation intensity of the carbon emission flow.

[0123] For each selected segment of traffic carbon emission flow and its corresponding digital resource scheduling sub-region, the scheduling direction and scale of digital resources are determined. The scheduling direction is determined based on the flow direction of the carbon emission flow. For example, if the carbon emission flow flows from region A to region B, the scheduling direction of digital resources may be from region B to region A to replenish the carbon emission flow and address its impact. The scheduling scale is determined based on the concentration intensity of the carbon emission flow. The higher the concentration intensity, the larger the scale of digital resources that need to be scheduled may be to meet the corresponding demand.

[0124] Step S137: Combine the scheduling direction and scale of digital resources, as well as the resource storage location information and circulation channel information in the digital resource scheduling field, to plan the scheduling path of digital resources.

[0125] Based on the determined scheduling direction and scale, as well as the resource storage location information and flow channel information in the digital resource scheduling field, the scheduling path of digital resources is planned. The scheduling path needs to consider the distribution of resource storage locations, the performance of flow channels (such as bandwidth and latency), and the requirements of scheduling direction and scale, to select the optimal path to ensure that resources can be efficiently and accurately scheduled from the starting position to the target position.

[0126] Step S138: Based on the planned digital resource scheduling path, determine each transfer node in the digital resource scheduling process, determine the node location, determine the resource handover method of each transfer node and the operation sequence of each transfer node. The handover method is represented by the data transmission protocol, and the operation sequence is defined by the timestamp sequence.

[0127] Based on the planned scheduling path, each transfer node in the digital resource scheduling process is identified. Transfer nodes are key locations in the resource scheduling path, such as resource storage nodes and relay nodes. The location coordinates of each node are determined, and the resource handover method and operation sequence of each transfer node are specified. The handover method is characterized by a data transmission protocol, such as TCP / IP or HTTP; the operation sequence is defined by a timestamp sequence, specifying the start time, duration, and completion time of each node's operation.

[0128] Step S139: Based on the scheduling direction, scheduling scale, scheduling path, transfer node and operation sequence, generate the basic text of digital resource scheduling instructions for each digital resource scheduling field sub-region.

[0129] Based on the determined scheduling direction, scheduling scale, scheduling path, transfer nodes, and operation sequence, a basic text of digital resource scheduling instructions is generated for each digital resource scheduling field sub-region. This basic text of digital resource scheduling instructions contains detailed instructions for scheduling digital resources within that sub-region.

[0130] Step S1391: Set the basic structure of the digital resource scheduling instruction basic text for each digital resource scheduling field sub-region. The basic structure includes an instruction header, an instruction body, and an instruction tail. The instruction header includes the region code and instruction number, the instruction body includes the scheduling core information, and the instruction tail includes the execution feedback content.

[0131] First, the basic structure of the digital resource scheduling instruction text is established, which consists of an instruction header, an instruction body, and an instruction tail. The instruction header contains a region code and an instruction number. The region code identifies the sub-region of the digital resource scheduling field to which the instruction belongs, and the instruction number is the unique identifier for the instruction. The instruction body contains core scheduling information, such as scheduling direction, scale, and path. The instruction tail contains execution feedback content, specifying the information that needs to be fed back after the instruction is executed.

[0132] Step S1392: Fill the region code and unique instruction number of each digital resource scheduling field sub-region into the instruction header. The instruction numbers are generated sequentially according to the generation order.

[0133] The area code of each digital resource scheduling field sub-area and the unique instruction number generated in the order of generation are filled into the instruction header to ensure that each instruction has a clear affiliation and identification.

[0134] Step S1393: In the instruction body, write the scheduling direction information of the digital resource, describing the specific azimuth direction of the digital resource from the starting position to the target position and the range of the movement trajectory of the resource from the starting position to the target position. The azimuth direction is characterized by the azimuth angle parameter, and the range of the movement trajectory is defined by the trajectory coordinate set.

[0135] The instruction body contains detailed information about the scheduling direction of the digital resource. It specifically describes the azimuth of the digital resource from its starting position to its target position, characterized by an azimuth parameter, such as 30 degrees east of north. Simultaneously, it describes the trajectory range of the resource's movement, defined by a set of trajectory coordinates, i.e., recording key coordinate points on the trajectory in sequence.

[0136] Step S1394: Write the scheduling scale information of digital resources, describing the type of digital resources to be scheduled, the quantity of digital resources to be scheduled, and the specification information of digital resources to be scheduled.

[0137] Write scheduling scale information, including the type of digital resource to be scheduled, such as traffic signal control resources and traffic information push resources; the quantity of digital resources to be scheduled, i.e. the specific number of each type of resource to be scheduled; and the specification information of the digital resources to be scheduled, such as the performance parameters and capacity of the resources.

[0138] Step S1395: Write the planned digital resource scheduling path information, list each segment and key node in the digital resource scheduling path in detail, determine the driving order of each segment and the passage requirements of each segment, the passage requirements include bandwidth requirements and latency requirements.

[0139] The planned scheduling path information is written, detailing each segment and key node in the path and specifying the travel order of each segment. Simultaneously, the requirements for each segment are determined, such as bandwidth requirements (the minimum data transmission bandwidth that the segment must meet) and latency requirements (the maximum allowable delay for resources to pass through the segment).

[0140] Step S1396: Write detailed information for each transfer node, including node name, node location, operation steps and operation standards for resource handover, define the connection relationship between different nodes through the node connection protocol, and represent the operation standards through operation process specification code.

[0141] Write detailed information for each transfer node, including node name and node location coordinates. Describe the operation steps for resource handover, i.e., the specific operation process required for the resource at that node. Simultaneously, define the operation standards for resource handover, which are represented by operation process specification code, corresponding to predefined standard operation procedures. Define the connection relationships between different nodes through a node connection protocol to ensure smooth resource handover between nodes.

[0142] Step S1397: Write operation timing information, specifying in detail the start time, duration, and completion time of each operation step.

[0143] Write operation timing information to specify a clear start time, duration, and completion time for each operation step, ensuring that all operations in the scheduling process can be performed in the predetermined time sequence.

[0144] Step S1398: At the end of the instruction, determine the feedback content and feedback method after the instruction is completed. The feedback content includes the actual number of resources scheduled, the actual time spent on resource scheduling, and the operation completion status of each node. The feedback method is represented by the data transmission interface type.

[0145] At the end of the instruction, the feedback content and method after instruction execution are determined. The feedback content includes information such as the actual number of resources scheduled, the actual time taken, and the operation completion status of each node. The feedback method is characterized by the data transmission interface type, such as using a REST API interface or a message queue interface.

[0146] Step S1399: Input the generated basic text of the digital resource scheduling instruction into the preset instruction verification module. The instruction verification module performs the following operations: According to the predefined instruction field template, check whether there are any missing fields in the basic text of the digital resource scheduling instruction. If so, call the default value or associated value of the corresponding field from the digital resource scheduling field to fill it in; According to the predefined scheduling logic rule base, check whether the order of scheduling steps meets the dependency relationship of resource flow and whether the node connection protocol is consistent. If a conflict is found, rearrange and update the step order or protocol description in the instruction text according to the priority defined in the scheduling logic rule base or the preset conflict resolution strategy, and format the basic text of the digital resource scheduling instruction according to the unified format specification. The format specification includes field alignment, character encoding format, and line break rules. After the formatting is completed, add the generation time identifier to the basic text of the digital resource scheduling instruction, forming the final basic text of the digital resource scheduling instruction for each sub-region of the digital resource scheduling field.

[0147] The generated basic text of the digital resource scheduling instruction is input into a preset instruction verification module. This module first checks for missing fields in the text based on a predefined instruction field template. If any are missing, it fills them with the default or associated values ​​from the digital resource scheduling field. Then, based on a predefined scheduling logic rule base, it checks whether the order of scheduling steps conforms to the resource flow dependencies and whether the node connection protocols are consistent. If conflicts are found, the order of steps or protocol descriptions in the instruction text are rearranged and updated according to the priority defined in the rule base or a preset conflict resolution strategy. Finally, the basic text of the instruction is formatted according to a unified format specification, including field alignment, character encoding format, line break rules, etc., and a generation time identifier is added to form the final basic text of the digital resource scheduling instruction.

[0148] Step S1310: Integrate the basic text of digital resource scheduling instructions from all digital resource scheduling field sub-regions, add instruction execution priority identifiers, and generate a set of digital resource scheduling instructions. The instruction execution priority identifiers are determined based on the size of the corresponding field coupling coefficient. The higher the field coupling coefficient, the higher the instruction execution priority.

[0149] The basic text of digital resource scheduling instructions from all digital resource scheduling sub-regions is integrated. Based on the field coupling coefficient corresponding to each instruction, an execution priority identifier is added to the instruction; the higher the field coupling coefficient, the higher the execution priority. All instructions with added priority identifiers are then aggregated to generate a digital resource scheduling instruction set.

[0150] Step S140: Transmit the set of digital resource scheduling instructions to the digital resource scheduling execution terminal, drive the digital resource scheduling execution action, and generate a set of digital resource scheduling execution trajectory information.

[0151] The generated set of digital resource scheduling instructions is transmitted to the digital resource scheduling execution terminal. The execution terminal, based on the instructions in the instruction set, drives digital resource scheduling actions, such as controlling traffic signals, pushing traffic information, and scheduling charging stations. During execution, the actual scheduling trajectory information of the digital resources is recorded, including the scheduling path, time, and resource quantity, generating a set of digital resource scheduling execution trajectory information.

[0152] Step S150: Based on the set of digital resource scheduling execution trajectory information, adjust the field parameters of the digital resource scheduling field to generate an optimization scheme for the digital resource scheduling field.

[0153] Based on the set of digital resource scheduling execution trajectory information, the deviation between the actual scheduling process and the expected scheduling is analyzed, and then the field parameters of the digital resource scheduling field are adjusted. By optimizing the field parameters, the efficiency and accuracy of digital resource scheduling are improved, and an optimized digital resource scheduling field scheme is generated.

[0154] Step S151: Extract the actual scheduling path information, the actual scheduling time information, and the actual scheduling resource quantity information from the digital resource scheduling execution trajectory information set.

[0155] Key information is extracted from the set of digital resource scheduling execution trajectory information, including actual scheduling path information, i.e. the path that the resource actually travels; actual scheduling time information, i.e. the start time, end time and time of each stage of the resource scheduling process; and actual scheduling resource quantity information, i.e. the quantity of various types of digital resources actually scheduled.

[0156] Step S152: Compare the actual scheduling path information with the planned scheduling path information in the corresponding digital resource scheduling instruction, analyze the path deviation characterization, and determine the degree of deviation between the deviation segments. The degree of deviation is characterized by the path offset distance parameter.

[0157] The actual scheduling path information is compared with the planned scheduling path information in the digital resource scheduling instructions to analyze the deviation between the two. Deviation segments are identified, i.e., segments where the actual path does not match the planned path. The degree of deviation is characterized by the path offset distance parameter, which is the distance between a point on the actual path and the corresponding point on the planned path.

[0158] Step S153: Compare the actual scheduling time information with the planned operation timing information in the corresponding digital resource scheduling instruction, analyze the time deviation characterization, determine the delayed execution steps and the delay duration of the delayed execution steps, and calculate the delay duration by the difference between the actual time and the planned time.

[0159] By comparing the actual scheduling time information with the planned operation sequence information, the time deviation is analyzed. The steps that are delayed in execution are identified, i.e., those whose actual execution time is later than the planned execution time. The delay duration is calculated as the difference between the actual execution time and the planned execution time.

[0160] Step S154: Compare the actual number of scheduled resources with the planned scheduling scale information in the corresponding digital resource scheduling instruction, analyze the quantity deviation characteristics, and determine the specific characteristics of insufficient resource scheduling and excessive resource scheduling.

[0161] By comparing the actual number of resources scheduled with the planned scheduling scale, the discrepancy is analyzed. Situations of insufficient resource scheduling (actual scheduling quantity is less than planned scheduling quantity) and excessive resource scheduling (actual scheduling quantity is greater than planned scheduling quantity) are identified, and specific characteristics are defined.

[0162] Step S155: Input the path deviation characterization, time deviation characterization, and quantity deviation characterization into the preset deviation attribution model. The deviation attribution model is trained based on historical scheduling data. Its input is various types of deviation data, and its output is the probability distribution of the preset deviation cause classification. The deviation cause classification includes: improper field parameter settings, scheduling path planning defects, and resource flow node operation deviation. The cause classification with the highest probability distribution is selected as the output result of this deviation analysis.

[0163] The path deviation, time deviation, and quantity deviation are input into a pre-defined deviation attribution model. This model is trained based on historical scheduling data, with various deviation data as input and a probability distribution of pre-defined deviation cause categories as output. Deviation cause categories include improper field parameter settings, scheduling path planning defects, and operational deviations at resource transfer nodes. The cause category with the highest probability in the probability distribution is selected as the output of this deviation analysis to determine the main causes of the deviation.

[0164] Step S156: Based on the output results of this deviation analysis, determine the field parameter types of the digital resource scheduling field that need to be adjusted. The field parameter types include scheduling potential energy, distribution density, and resource flow efficiency.

[0165] Based on the output of the deviation analysis, the main causes of the deviation are identified, and then the types of field parameters of the digital resource scheduling field that need to be adjusted are determined. For example, if the deviation is caused by improper field parameter settings, it may be necessary to adjust parameters such as scheduling potential energy, distribution density, or resource flow efficiency.

[0166] Step S157: Calculate the adjustment range of each field parameter that needs to be adjusted according to the degree of deviation. The adjustment range is determined based on the severity of the deviation and the degree of influence of the field parameter on the scheduling result.

[0167] Based on the magnitude of the deviation and the degree of influence of the field parameters on the scheduling results, the adjustment range for each field parameter that needs to be adjusted is calculated. The more severe the deviation, the larger the adjustment range may be; the parameter with the greater influence on the scheduling results may also have a correspondingly larger adjustment range.

[0168] Step S158: Adjust the corresponding field parameters according to the calculated adjustment range to generate an adjusted digital resource scheduling field. Based on the adjusted digital resource scheduling field, process the resource distribution of each scheduling area and the flow channel information of each scheduling area to form a digital resource scheduling field adjustment description. The digital resource scheduling field adjustment description includes the values ​​of the parameters before and after the adjustment and the basis for the adjustment.

[0169] According to the calculated adjustment range, the corresponding field parameters are adjusted. After the adjustment is completed, an adjusted digital resource scheduling field is generated. Based on the adjusted scheduling field, the resource distribution and flow channel information of each scheduling area are processed to form a digital resource scheduling field adjustment description. This digital resource scheduling field adjustment description includes the values ​​of the parameters before and after the adjustment, as well as the basis for the adjustment, such as the deviation analysis results and the calculation process of the adjustment range.

[0170] For example, step S1581: parse the field parameter structure of the digital resource scheduling field, and extract the current value of each field parameter, the value range of each field parameter, and the adjustment authority of each field parameter.

[0171] Before adjusting the field parameters, the field parameter structure of the digital resource scheduling field is first analyzed. The current value of each field parameter is extracted to understand the current status of the parameter; the value range of each parameter is clarified to ensure that the adjusted parameter will not exceed a reasonable range; at the same time, the adjustment authority for each parameter is determined, i.e., whether adjustment is allowed and the approval process for adjustment, etc.

[0172] Step S1582: Based on the determined types of field parameters that need to be adjusted and the corresponding adjustment range, generate a corresponding parameter adjustment plan table. The parameter adjustment plan table includes parameter name, current value, adjustment range, target value, and adjustment order.

[0173] Generate a parameter adjustment plan table based on the required field parameter types and corresponding adjustment ranges. This parameter adjustment plan table includes information such as parameter name, current value, adjustment range, target value, and adjustment order.

[0174] Step S1583: According to the adjustment order in the parameter adjustment plan table, adjust each field parameter that needs to be adjusted in turn. During the adjustment of each field parameter, based on the predefined parameter association diagram in the digital resource scheduling field, obtain the associated parameters that are directly related to the currently adjusted parameter in real time; monitor the value changes of the associated parameters; when the value of any associated parameter exceeds its preset value range, pause the current parameter adjustment operation; according to the parameter association diagram, trace back the adjustment operation chain that caused the associated parameter to go out of bounds, identify the affected associated parameters, record this linkage relationship, and adjust the adjustment range of the current parameter.

[0175] Following the adjustment sequence in the parameter adjustment plan, each field parameter requiring adjustment is adjusted sequentially. During the adjustment process, based on the predefined parameter correlation diagram in the digital resource scheduling field, related parameters directly correlated with the currently adjusted parameter are acquired in real time. Changes in the values ​​of these related parameters are monitored, and if the value of any related parameter exceeds its preset range, the current parameter adjustment operation is immediately paused. Based on the parameter correlation diagram, the adjustment operation chain that caused the related parameter to exceed its limit is traced back, the affected related parameters are identified, and the aforementioned linkage relationship is recorded. Then, the adjustment range of the current parameter is adjusted to prevent related parameters from exceeding their limits again.

[0176] Step S1584: After completing a single parameter adjustment, calculate the predicted value of the field coupling coefficient corresponding to the adjusted field parameters. The predicted value is obtained based on the correlation between the adjusted parameters and the characteristics of traffic carbon emission flow.

[0177] After a single parameter adjustment, the predicted field coupling coefficient is calculated based on the correlation between the adjusted field parameters and the characteristics of traffic carbon emission flows. This predicted field coupling coefficient reflects the expected impact of the adjusted parameters on the degree of correlation between the traffic carbon emission flow set and the digital resource scheduling field.

[0178] Step S1585: Compare the predicted value of the field coupling coefficient with the actual coupling coefficient before adjustment, and determine whether the predicted value of the field coupling coefficient is better than the actual coupling coefficient before adjustment. If the predicted value of the field coupling coefficient is not better than the actual coupling coefficient before adjustment, cancel the current parameter adjustment and regenerate the adjustment scheme for the parameter. If the predicted value of the field coupling coefficient is better than the actual coupling coefficient before adjustment, confirm that the current parameter adjustment is effective, and record the adjusted parameter value and the corresponding predicted value of the field coupling coefficient.

[0179] Compare the predicted field coupling coefficient with the actual coupling coefficient before adjustment. If the predicted value is better than the actual coupling coefficient, it indicates that the parameter adjustment has a positive effect on improving the correlation between the two, confirming the adjustment's effectiveness, and record the adjusted parameter values ​​and corresponding predicted values. If the predicted value is not better than the actual coupling coefficient, cancel the parameter adjustment, re-analyze the reasons for the deviation, and generate a new parameter adjustment scheme.

[0180] Step S1586: Repeat the above parameter adjustment, monitoring, verification and confirmation steps to complete the adjustment of all field parameters that need to be adjusted, integrate all adjusted field parameters, update the field structure of the digital resource scheduling field and the association information of each digital resource scheduling subfield, and generate the adjusted digital resource scheduling field.

[0181] Repeat the steps of parameter adjustment, monitoring associated parameters, verifying the predicted coupling coefficients, and confirming the effectiveness of the adjustments until all field parameters requiring adjustment are adjusted. Integrate all adjusted field parameters, update the field structure of the digital resource scheduling field and the association information between each digital resource scheduling subfield, and finally generate the adjusted digital resource scheduling field.

[0182] Step S159: Integrate the adjusted digital resource scheduling field, the explanation of the adjustment range of the field parameters, and the explanation of the adjustment of the digital resource scheduling field to generate an optimization scheme for the digital resource scheduling field.

[0183] The adjusted digital resource scheduling field, the explanation of the adjustment range of the field parameters, and the explanation of the digital resource scheduling field adjustment are integrated to form a digital resource scheduling field optimization scheme. This digital resource scheduling field optimization scheme includes the adjusted scheduling field information, the specific details of the parameter adjustment, and the basis and explanation of the adjustment.

[0184] Based on the same inventive concept, please refer to Figure 2 This paper shows a schematic block diagram of a digital resource scheduling and management system 100 for urban transportation, which is provided in an embodiment of this application and is used to execute the above-described digital resource scheduling and management method for urban transportation. The digital resource scheduling and management system 100 for urban transportation may include a communication unit 110, a machine-readable storage medium 120, and a processor 130.

[0185] In this embodiment, both the machine-readable storage medium 120 and the processor 130 are located within the digital resource scheduling and management system 100 applied to the urban transportation field and are separately configured. However, it should be understood that the machine-readable storage medium 120 may also be independent of the digital resource scheduling and management system 100 applied to the urban transportation field and may be accessed by the processor 130 through a bus interface. Alternatively, the machine-readable storage medium 120 may also be integrated into the processor 130 and may communicate and interact with external systems through the communication unit 110.

[0186] The processor 130 is the control center of the digital resource scheduling and management system 100 applied to the urban transportation field. It connects various parts of the system via various interfaces and lines, and performs various functions and processes data by running or executing software programs and / or modules stored in the machine-readable storage medium 120 and calling data stored in the machine-readable storage medium 120, thereby providing overall monitoring of the system. Optionally, the processor 130 may include one or more processing cores; for example, it may integrate an application processor and a modem processor. The application processor mainly handles the operating system, user interface, and applications, while the modem processor mainly handles wireless communication. It is understood that the modem processor may not be integrated into the processor. The machine-readable storage medium 120 stores machine-executable instructions for implementing the scheme of this application, and the processor 130 executes the machine-executable instructions stored in the machine-readable storage medium 120 to implement the digital resource scheduling and management method for the urban transportation field provided in the aforementioned method embodiments.

[0187] It should be noted that, in order to simplify the description of the present invention and thus help to understand one or more embodiments of the invention, multiple features may sometimes be grouped into one embodiment, drawing or description thereof in the foregoing description of the embodiments of the present invention.

Claims

1. A digital resource scheduling and management method applied in the field of urban transportation, characterized in that, The method includes: A traffic carbon emission flow set is constructed in the urban transportation field. The traffic carbon emission flow set integrates carbon emission information from fixed monitoring points, carbon emission information from mobile transportation carriers, and traffic operation status information. The traffic operation status information includes traffic flow information of different road segments, traffic flow information of different areas, traffic efficiency information of different road segments, traffic efficiency information of different areas, traffic congestion status information of different road segments, and traffic congestion status information of different areas in the urban transportation field. Based on the spatiotemporal distribution characteristics of the traffic carbon emission flow set, a digital resource scheduling field is generated. The digital resource scheduling field includes the distribution status information of urban traffic digital resources and the scheduling potential information of urban traffic digital resources. The urban traffic digital resources include traffic signal control resources, traffic information push resources and charging pile scheduling resources. A field coupling relationship is established between the traffic carbon emission flow set and the digital resource scheduling field to generate a digital resource scheduling instruction set. The field coupling relationship is represented by an association mapping matrix between key feature parameters in the traffic carbon emission flow set and key field parameters in the digital resource scheduling field. The row dimension of the association mapping matrix is ​​the key feature parameter of the carbon emission flow, and the column dimension is the key field parameter, so as to determine the corresponding association between different feature parameters and different field parameters. The set of digital resource scheduling instructions is transmitted to the digital resource scheduling execution terminal to drive the digital resource scheduling execution action and generate a set of digital resource scheduling execution trajectory information, which includes actual scheduling path information, actual scheduling time information and actual scheduling resource quantity information. Based on the set of digital resource scheduling execution trajectory information, the field parameters of the digital resource scheduling field are adjusted to generate an optimized scheme for the digital resource scheduling field. The field parameters of the digital resource scheduling field include the scheduling potential energy of digital resources, the distribution density of digital resources, and the circulation efficiency of digital resources. The circulation efficiency is characterized by the amount of resources circulating per unit time.

2. The digital resource scheduling and management method applied to urban transportation as described in claim 1, characterized in that, The construction of the traffic carbon emission stream set in the urban transportation sector includes: By connecting to the fixed carbon emission monitoring network, carbon emission monitoring information from different fixed monitoring points in the urban transportation sector is collected over a continuous period of time. The temporal variation trend of carbon emission information at each fixed monitoring point and the spatial coverage of carbon emission information at each fixed monitoring point are extracted to form a subset of carbon emission characteristics of fixed monitoring points. By connecting to the mobile transportation carrier monitoring system, real-time carbon emission information and driving trajectory information of different types of mobile transportation carriers in the urban transportation field are collected during the driving process. The trajectory correlation characteristics of carbon emission information of each type of mobile transportation carrier and the emission intensity change trend of carbon emission information of each type of mobile transportation carrier are extracted to form a subset of carbon emission characteristics of mobile transportation carriers. By connecting to the traffic operation monitoring platform, traffic flow information, traffic flow information, traffic efficiency information, traffic congestion status information, and traffic congestion status information of different road sections, different areas, and different regions within the urban traffic field are collected. The temporal correlation coefficients of traffic flow information, traffic efficiency information, and traffic congestion status information in the same area at different time periods are calculated to generate a temporal correlation set. The spatial correlation coefficients of the above information among traffic flow information, traffic efficiency information, and traffic congestion status information in the same time period are calculated to generate a spatial correlation set. The temporal correlation set and the spatial correlation set are integrated to form a subset of traffic operation status features. The carbon emission feature subset of fixed monitoring points is divided into zones according to spatial coverage, the zone boundaries are determined, and a set of fixed carbon emission feature for each zone is generated. Each zone corresponds to a complete set of carbon emission feature information of fixed monitoring points. The carbon emission feature subset of mobile transportation vehicles is divided into regions according to the area to which the driving trajectory belongs. The matching is completed by comparing the trajectory coordinates with the boundary coordinates of the regions, and a set of mobile carbon emission feature data for each region is generated. Each region corresponds to a complete set of carbon emission feature information of mobile transportation vehicles. The traffic operation status feature subset is divided into regions, and the boundary standard consistent with that of the fixed monitoring point division is used to generate a set of traffic operation status features for each region. Each region corresponds to a complete set of traffic operation status feature information. The fixed carbon emission feature set of a region is fused with the mobile carbon emission feature set of the corresponding region to generate a comprehensive carbon emission feature set of the region. By associating and integrating the comprehensive carbon emission feature set of each region with the corresponding regional traffic operation status feature set, a mapping relationship between the two is established, and a subset of regional traffic carbon emission flows is generated. Add regional coding identifiers and timestamp identifiers to each traffic carbon emission stream subset of each zone to determine the spatial range and time span corresponding to each traffic carbon emission stream subset of each zone; Integrate all subsets of regional traffic carbon emission flows with regional coding and timestamp identifiers to form a traffic carbon emission flow set.

3. The digital resource scheduling and management method applied to urban transportation as described in claim 1, characterized in that, The generation of a digital resource scheduling field based on the spatiotemporal distribution characteristics of the traffic carbon emission flow set includes: Extract the temporal distribution characteristics of the traffic carbon emission flow set, determine the activity level of the traffic carbon emission flow in different time periods, and form a set of carbon emission flow temporal activity characteristics; Extract the spatial distribution characteristics of the traffic carbon emission flow set, determine the degree of aggregation of traffic carbon emission flows in different regions, and form a set of spatial aggregation characteristics of carbon emission flows. By integrating the temporal activity feature set and the spatial clustering feature set of carbon emission flows, a spatiotemporal distribution feature set of carbon emission flows is generated. By connecting to the digital resource management platform, information on the type of all schedulable digital resources in the urban transportation sector, the storage location information of all schedulable digital resources, and the availability status information of all schedulable digital resources are collected. The types of schedulable digital resources cover traffic signal control resources, traffic information push resources, and charging pile scheduling resources, forming a set of basic digital resource information. Based on the set of basic information of digital resources, the service range characteristics and scheduling response characteristics of each digital resource are extracted to form a set of digital resource scheduling characteristics. The service range characteristics are defined by geographical coordinates, and the scheduling response characteristics are characterized by response time parameters. The spatiotemporal distribution feature set of carbon emission streams is matched with the feature set of digital resource scheduling. The similarity between the spatiotemporal feature vector of carbon emission streams and the feature vector of digital resource scheduling is calculated. Feature pairs with similarity higher than a preset threshold are marked as fitting relationships, and a list of fitting relationships is generated. Based on the adaptation relationship list, digital resource scheduling areas are divided, and the area boundaries are determined according to the differences in the type of adaptation relationship and the geographical continuity of the area. Each digital resource scheduling area corresponds to a set of adapted carbon emission flow spatiotemporal distribution characteristics and digital resource types. The distribution density of digital resources in each digital resource scheduling area is normalized to generate a digital resource distribution density index; the carbon emission flow activity level in each digital resource scheduling area is normalized to generate a carbon emission flow activity index; based on the digital resource distribution density index and the carbon emission flow activity index, the initial value of the scheduling potential energy of digital resources in each digital resource scheduling area is determined through a correlation function. Based on the digital resource distribution density of each digital resource scheduling region, the initial value of the scheduling potential energy of each digital resource scheduling region, and the spatiotemporal distribution characteristics of the adapted carbon emission flow, a digital resource scheduling subfield is constructed for each digital resource scheduling region. The digital resource scheduling subfield includes a resource distribution layer, a potential energy gradient layer, and an adaptation relationship layer. The digital resource scheduling subfields of all digital resource scheduling areas are integrated, and the boundary association information and resource flow channel information between each digital resource scheduling subfield are supplemented to generate a digital resource scheduling field. The resource flow channel information is characterized by channel bandwidth and transmission delay parameters.

4. The digital resource scheduling and management method applied to urban transportation as described in claim 1, characterized in that, The process of establishing the field coupling relationship between the traffic carbon emission flow set and the digital resource scheduling field, and generating a digital resource scheduling instruction set, includes: Key characteristic parameters are extracted from the traffic carbon emission flow set. These key characteristic parameters include the flow rate of the carbon emission flow, the aggregation intensity of the carbon emission flow, and the spatiotemporal diffusion direction of the carbon emission flow. The flow rate is calculated by the spatial displacement of the carbon emission flow per unit time, and the aggregation intensity is characterized by the concentration value of the carbon emission flow per unit space. Key field parameters are extracted from the digital resource scheduling field. These key field parameters include the scheduling potential energy of digital resources, the distribution density of digital resources, and the circulation efficiency of digital resources. The circulation efficiency is calculated by the amount of resources circulating per unit time. A correlation mapping matrix is ​​constructed between key characteristic parameters of traffic carbon emission flows and key field parameters of digital resource scheduling fields. The row dimension of the correlation mapping matrix is ​​the key characteristic parameters of carbon emission flows, and the column dimension is the key field parameters, so as to determine the corresponding correlation between different characteristic parameters and different field parameters. Based on the correlation mapping matrix, the field coupling coefficient between the traffic carbon emission flow set and the digital resource scheduling field is calculated. The field coupling coefficient characterizes the degree of correlation between the traffic carbon emission flow set and the digital resource scheduling field. Obtain a preset coupling coefficient threshold, compare the field coupling coefficient with the coupling coefficient threshold, and filter out traffic carbon emission flow segments and their mapped digital resource scheduling field sub-regions with field coupling coefficients higher than the coupling coefficient threshold; For each selected segment of traffic carbon emission flow and its corresponding digital resource scheduling field sub-region, the scheduling direction and scale of digital resources are determined. The scheduling direction is determined based on the flow direction of the carbon emission flow, and the scheduling scale is determined based on the aggregation intensity of the carbon emission flow. By combining the scheduling direction and scale of digital resources, as well as the resource storage location information and circulation channel information in the digital resource scheduling field, the scheduling path of digital resources is planned. Based on the planned digital resource scheduling path, each transfer node in the digital resource scheduling process is determined, the node location is determined, the resource handover method of each transfer node and the operation sequence of each transfer node are determined, the handover method is represented by the data transmission protocol, and the operation sequence is defined by the timestamp sequence; Based on the scheduling direction, scheduling scale, scheduling path, transfer nodes, and operation sequence, generate the basic text of digital resource scheduling instructions for each digital resource scheduling field sub-region; The basic text of digital resource scheduling instructions from all digital resource scheduling field sub-regions is integrated, and instruction execution priority identifiers are added to generate a set of digital resource scheduling instructions. The instruction execution priority identifiers are determined based on the magnitude of the corresponding field coupling coefficient. The higher the field coupling coefficient, the higher the instruction execution priority.

5. The digital resource scheduling and management method applied to urban transportation as described in claim 3, characterized in that, The process of dividing digital resource scheduling areas based on the adaptation relationship list includes: Based on the spatiotemporal distribution characteristics of carbon emission flows, the urban transportation sector is initially divided into zones to ensure that the spatiotemporal distribution characteristics of carbon emission flows are consistent within each initial zone. Extract the first core information of the spatiotemporal distribution characteristics of carbon emission flows in each preliminary partition to form the feature identifier of each preliminary partition. The first core information includes the feature peak, feature duration, and feature spatial coverage. Based on the set of digital resource scheduling features, digital resources are classified and grouped, and the second core information of the scheduling features of each group of digital resources is extracted to form the resource identifier of each group of digital resources. The second core information includes the service range radius, scheduling response time, and resource capacity. The feature identifier of each preliminary partition is matched with the resource identifier of each group of digital resources, and the corresponding digital resource group is matched for each preliminary partition according to the determined adaptation relationship. The number of matching digital resource groups within each preliminary partition is counted, and the standard deviation of the storage location coordinates of each digital resource group within each preliminary partition is calculated and defined as the resource distribution uniformity parameter. The resource distribution uniformity parameter of each preliminary partition is compared with a preset dispersion threshold. When the resource distribution uniformity parameter of any preliminary partition is greater than the dispersion threshold, a spatial clustering algorithm is used to spatially divide the preliminary partition based on the storage location coordinates of the digital resource groups within that preliminary partition, generating multiple subdivided regions. When the lists of digital resource group identifiers that match between two adjacent preliminary partitions are completely identical, the Euclidean distance between the spatiotemporal distribution feature vectors of carbon emission flows of the two preliminary partitions is calculated and defined as the feature difference parameter; the feature difference parameter is compared with a preset merging threshold; when the feature difference parameter is less than the merging threshold, the two adjacent preliminary partitions are merged into one partition. For each adjusted partition, the core information of its spatiotemporal distribution characteristics of carbon emission flow is re-extracted and matched with the digital resource group information of each adjusted partition. The re-extracted information is substituted into the preset adaptation verification model to generate adaptation verification results. A unique regional code is assigned to each partition whose adaptation verification results meet the requirements. The regional code includes the spatial location information of the partition and the main adaptation resource type information of the partition. Record the regional code of each partition, the core information of the spatiotemporal distribution characteristics of carbon emission flows of each partition, the digital resource group information matched by each partition, and the regional boundary information of each partition. The regional boundary information is represented by a geographic coordinate sequence.

6. The digital resource scheduling and management method applied to urban transportation as described in claim 4, characterized in that, The basic text of digital resource scheduling instructions for each digital resource scheduling field sub-region is generated based on scheduling direction, scheduling scale, scheduling path, transfer nodes, and operation sequence, including: Define the basic structure of the digital resource scheduling instruction basic text for each digital resource scheduling field sub-region. The basic structure includes an instruction header, an instruction body, and an instruction tail. The instruction header includes the region code and instruction number, the instruction body includes the core scheduling information, and the instruction tail includes the execution feedback content. Fill the region code and unique instruction number of each digital resource scheduling field sub-region into the instruction header. The instruction numbers are generated sequentially according to the generation order. The instruction body contains the scheduling direction information of the digital resource, which describes the specific orientation of the digital resource from the starting position to the target position and the range of the movement trajectory of the resource from the starting position to the target position. The orientation is characterized by the azimuth angle parameter, and the range of the movement trajectory is defined by the trajectory coordinate set. Write the scheduling scale information of digital resources, describing the type of digital resources to be scheduled, the quantity of digital resources to be scheduled, and the specifications of digital resources to be scheduled; The digital resource scheduling path information is written into the plan, which lists each segment and key node in the digital resource scheduling path in detail, determines the driving order of each segment and the passage requirements of each segment, including bandwidth requirements and latency requirements. Write detailed information for each flow node, including node name, node location, resource handover operation steps and operation standards. Define the connection relationship between different nodes through the node connection protocol, and represent the operation standards through operation process specification code. Write operation timing information, specifying in detail the start time, duration, and completion time of each operation step; At the end of the instruction, the feedback content and feedback method after the instruction is completed are determined. The feedback content includes the actual number of resources scheduled, the actual time spent on resource scheduling, and the operation completion status of each node. The feedback method is represented by the data transmission interface type. The generated basic text of the digital resource scheduling instruction is input into a preset instruction verification module. The instruction verification module performs the following operations: according to the predefined instruction field template, it checks whether there are any missing fields in the basic text of the digital resource scheduling instruction. If so, it calls the default value or associated value of the corresponding field from the digital resource scheduling field to fill it in; according to the predefined scheduling logic rule base, it checks whether the order of scheduling steps satisfies the dependency relationship of resource flow and whether the node connection protocol is consistent. If a conflict is found, it rearranges and updates the step order or protocol description in the instruction text according to the priority defined in the scheduling logic rule base or the preset conflict resolution strategy, and typesets the basic text of the digital resource scheduling instruction according to a unified format specification, which includes field alignment, character encoding format, and line break rules. After the basic text of the digital resource scheduling instruction is typeset, a generation time identifier is added to form the final basic text of the digital resource scheduling instruction for each sub-region of the digital resource scheduling field.

7. The digital resource scheduling and management method applied to urban transportation as described in claim 1, characterized in that, The step of adjusting the field parameters of the digital resource scheduling field based on the set of digital resource scheduling execution trajectory information to generate an optimized digital resource scheduling field includes: Extract the actual scheduling path information, actual scheduling time information, and actual scheduling resource quantity information from the digital resource scheduling execution trajectory information set; The actual scheduling path information is compared with the planned scheduling path information in the corresponding digital resource scheduling instruction. The path deviation characterization is analyzed, and the deviation of the deviation segment and the degree of deviation are determined. The degree of deviation is characterized by the path offset distance parameter. The actual scheduling time information is compared with the planned operation timing information in the corresponding digital resource scheduling instruction. The time deviation characterization is analyzed to determine the delayed execution steps and the delay duration of the delayed execution steps. The delay duration is calculated by the difference between the actual time and the planned time. By comparing the actual number of resources scheduled with the planned scheduling scale information in the corresponding digital resource scheduling instructions, the characteristics of the quantity deviation are analyzed to determine the specific characteristics of insufficient resource scheduling and excessive resource scheduling. The path deviation characterization, time deviation characterization, and quantity deviation characterization are input into a preset deviation attribution model. The deviation attribution model is trained based on historical scheduling data. Its input is various types of deviation data, and its output is the probability distribution of preset deviation cause classification. The deviation cause classification includes: improper field parameter settings, scheduling path planning defects, and resource flow node operation deviation. The cause classification with the highest probability distribution is selected as the output result of this deviation analysis. Based on the output of this deviation analysis, the types of field parameters of the digital resource scheduling field that need to be adjusted are determined. The types of field parameters include scheduling potential energy, distribution density, and resource flow efficiency. Based on the degree of deviation, the adjustment range for each field parameter that needs to be adjusted is calculated. The adjustment range is determined based on the severity of the deviation and the degree of influence of the field parameter on the scheduling result. According to the calculated adjustment range, the corresponding field parameters are adjusted to generate an adjusted digital resource scheduling field. Based on the adjusted digital resource scheduling field, the resource distribution of each scheduling area and the flow channel information of each scheduling area are processed to form a digital resource scheduling field adjustment description. The digital resource scheduling field adjustment description includes the values ​​of the parameters before and after the adjustment and the basis for the adjustment. The integrated and adjusted digital resource scheduling field, the explanation of the adjustment range of field parameters, and the explanation of the adjustment of digital resource scheduling field are used to generate an optimization scheme for the digital resource scheduling field.

8. The digital resource scheduling and management method applied to urban transportation as described in claim 2, characterized in that, The process of associating and integrating the comprehensive carbon emission feature set of a region with the corresponding regional traffic operation status feature set to establish a mapping relationship between the two and generate a subset of regional traffic carbon emission flows includes: Extract the peak carbon emission intensity information, the duration of carbon emissions, and the spatial diffusion range of carbon emissions from the integrated carbon emission feature set of the region; Extract peak traffic flow information, traffic congestion duration information, and traffic flow spatial distribution range information from the regional traffic operation status feature set; The peak carbon emission intensity information and peak traffic flow information are time-aligned to determine the temporal correspondence between the two. By comparing and analyzing the duration of carbon emissions with the duration of traffic congestion, the correlation characteristics between the two over time can be determined. The spatial overlay process is performed on the spatial diffusion range information of carbon emissions and the spatial distribution range information of traffic flow to determine the overlapping area of ​​the two in the spatial dimension and the spatial coverage relationship between the two in the spatial dimension. Using time correspondence, time span, and spatial coverage as input features, and historical carbon emission and traffic operation status association labels as training targets, a neural network model is constructed and trained to serve as the association model between regional comprehensive carbon emission features and regional traffic operation status features. Substitute the specific data from the corresponding region's comprehensive carbon emission feature set and the corresponding region's traffic operation status feature set into the correlation model to generate correlation data between carbon emissions and traffic operation status within the region. Extract the core correlation information from the correlation data. The core correlation information includes how changes in carbon emissions affect traffic operation status and how traffic operation status responds to carbon emissions. The core correlation information is integrated with the corresponding regional comprehensive carbon emission feature set and the corresponding regional traffic operation status feature set to form the basic data framework of regional traffic carbon emission flow. The basic data framework includes data field definitions and data association rules. By supplementing the basic data framework with regional identifiers, time identifiers, and data source identifiers, a subset of traffic carbon emission streams by region is generated.

9. The digital resource scheduling and management method applied to urban transportation as described in claim 3, characterized in that, The distribution density of digital resources in each digital resource scheduling area is normalized to generate a digital resource distribution density index; the carbon emission flow activity level in each digital resource scheduling area is normalized to generate a carbon emission flow activity index. Based on the digital resource distribution density index and the carbon emission flow activity index, the initial value of the scheduling potential energy of digital resources within each digital resource scheduling area is determined through a correlation function, including: Count the number of various types of digital resources in each digital resource scheduling area, and determine the total number of digital resources in each digital resource scheduling area; Measure the spatial area of ​​each digital resource scheduling area, calculate the number of digital resources per unit area in each digital resource scheduling area, and use the number of digital resources per unit area as the initial value of the distribution density of digital resources in that digital resource scheduling area; Obtain the location coordinates of all digital resource storage points within each digital resource scheduling area; define a neighborhood with a fixed radius centered on each resource storage point, count the number of resource storage points in each neighborhood, calculate the average number of resource points in all neighborhoods, and define it as the local cluster density; compare the local cluster density with a preset distribution threshold; when the local cluster density is lower than the distribution threshold, multiply the initial distribution density value by a density correction coefficient greater than 1 to obtain the digital resource distribution density correction value, wherein the density correction coefficient is negatively correlated with the spatial standard deviation of the resource storage point coordinates; The digital resource distribution density correction value is normalized to generate a dimensionless digital resource distribution density index. The activity level parameter is extracted from the spatiotemporal distribution feature set of carbon emission flows in each digital resource scheduling region. The activity level parameter includes the flow rate of carbon emission flows and the aggregation intensity of carbon emission flows. The activity level parameter is normalized to generate a dimensionless carbon emission flow activity index. A correlation function between the digital resource distribution density index and the carbon emission flow activity index is constructed. The correlation function reflects the combined effect of the two on the scheduling potential. The correlation function adopts a linear weighted function. Substitute the digital resource distribution density index and the carbon emission flow activity index of each digital resource scheduling region into the correlation function to calculate the initial value of the digital resource scheduling potential energy of each digital resource scheduling region. When the initial value of the scheduling potential energy exceeds the reasonable range threshold, adjust the parameter weights in the correlation function, resubmit them into the correlation function to calculate the initial value of the scheduling potential energy, until the calculation result falls within the reasonable range threshold. Record the digital resource distribution density correction value, carbon emission flow activity index, and initial scheduling potential energy value of each digital resource scheduling area to form the basic data of scheduling potential energy for each digital resource scheduling area.

10. A digital resource scheduling and management system applied in the field of urban transportation, characterized in that, include: processor; A machine-readable storage medium for storing machine-executable instructions of the processor; The processor is configured to execute the digital resource scheduling and management method for urban transportation as described in any one of claims 1 to 9 by executing the machine-executable instructions.

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